import pandas as pd
import numpy as np
from pmdarima import auto_arima
import plotly.graph_objects as go
from sklearn.metrics import mean_absolute_error, mean_squared_error
import os
# !pip install -U kaleido # you need to install for the visualization
# Read the Excel file
# Data_Status: Indicates the status of the data. The value "2020F" suggests that it is a forecast for the year 2020.
# State: Represents the state for which the data is recorded (in this case, "CA" for California).
# MSN: Stands for "Monthly State Names" and refers to the specific energy metric or variable being measured. Examples include ARICD, ARICV, ARTCD, ARTCV, ARTXD, WWTXV, WXICD, WXICV, ZWCDP, ZWHDP.
df = pd.read_csv('Datasets/pr_CA.csv')
df.drop('Data_Status',axis=1,inplace=True)
df
| State | MSN | 1970 | 1971 | 1972 | 1973 | 1974 | 1975 | 1976 | 1977 | ... | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | CA | ARICD | 0.49 | 0.64 | 0.64 | 0.69 | 1.47 | 1.62 | 1.60 | 1.70 | ... | 14.83 | 16.79 | 15.59 | 15.97 | 13.57 | 10.04 | 9.90 | 12.56 | 13.42 | 11.36 |
| 1 | CA | ARICV | 39.00 | 51.90 | 52.30 | 60.50 | 115.90 | 141.00 | 149.60 | 169.10 | ... | 891.20 | 903.80 | 937.00 | 916.30 | 834.50 | 614.10 | 592.70 | 758.60 | 744.60 | 636.30 |
| 2 | CA | ARTCD | 0.49 | 0.64 | 0.64 | 0.69 | 1.47 | 1.62 | 1.60 | 1.70 | ... | 14.83 | 16.79 | 15.59 | 15.97 | 13.57 | 10.04 | 9.90 | 12.56 | 13.42 | 11.36 |
| 3 | CA | ARTCV | 39.00 | 51.90 | 52.30 | 60.50 | 115.90 | 141.00 | 149.60 | 169.10 | ... | 891.20 | 903.80 | 937.00 | 916.30 | 834.50 | 614.10 | 592.70 | 758.60 | 744.60 | 636.30 |
| 4 | CA | ARTXD | 0.49 | 0.64 | 0.64 | 0.69 | 1.47 | 1.62 | 1.60 | 1.70 | ... | 14.83 | 16.79 | 15.59 | 15.97 | 13.57 | 10.04 | 9.90 | 12.56 | 13.42 | 11.36 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 299 | CA | WWTXV | 55.40 | 57.00 | 61.70 | 64.40 | 70.80 | 67.40 | 77.70 | 83.90 | ... | 366.10 | 343.30 | 409.30 | 410.50 | 190.40 | 163.30 | 171.50 | 188.80 | 215.50 | 198.10 |
| 300 | CA | WXICD | 4.14 | 3.97 | 4.23 | 4.63 | 4.63 | 4.95 | 5.51 | 6.26 | ... | 35.56 | 34.62 | 33.37 | 33.91 | 32.88 | 32.66 | 32.69 | 32.94 | 29.79 | 26.87 |
| 301 | CA | WXICV | 10.50 | 10.10 | 11.00 | 15.50 | 15.20 | 14.50 | 21.70 | 20.60 | ... | 70.50 | 69.60 | 74.20 | 67.60 | 54.80 | 56.60 | 44.10 | 54.20 | 41.20 | 32.80 |
| 302 | CA | ZWCDP | 748.00 | 738.00 | 748.00 | 681.00 | 750.00 | 596.00 | 651.00 | 739.00 | ... | 774.00 | 1020.00 | 965.00 | 1175.00 | 1158.00 | 1024.00 | 1166.00 | 1102.00 | 894.00 | 1206.00 |
| 303 | CA | ZWHDP | 3169.00 | 3690.00 | 3278.00 | 3396.00 | 3297.00 | 3609.00 | 3031.00 | 3050.00 | ... | 3182.00 | 2741.00 | 2707.00 | 2091.00 | 2342.00 | 2438.00 | 2442.00 | 2539.00 | 2946.00 | 2564.00 |
304 rows × 53 columns
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 304 entries, 0 to 303 Data columns (total 53 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 State 304 non-null object 1 MSN 304 non-null object 2 1970 284 non-null float64 3 1971 284 non-null float64 4 1972 284 non-null float64 5 1973 284 non-null float64 6 1974 284 non-null float64 7 1975 284 non-null float64 8 1976 284 non-null float64 9 1977 284 non-null float64 10 1978 284 non-null float64 11 1979 284 non-null float64 12 1980 284 non-null float64 13 1981 284 non-null float64 14 1982 284 non-null float64 15 1983 284 non-null float64 16 1984 284 non-null float64 17 1985 284 non-null float64 18 1986 284 non-null float64 19 1987 284 non-null float64 20 1988 284 non-null float64 21 1989 284 non-null float64 22 1990 284 non-null float64 23 1991 284 non-null float64 24 1992 284 non-null float64 25 1993 280 non-null float64 26 1994 280 non-null float64 27 1995 280 non-null float64 28 1996 280 non-null float64 29 1997 283 non-null float64 30 1998 283 non-null float64 31 1999 283 non-null float64 32 2000 283 non-null float64 33 2001 283 non-null float64 34 2002 283 non-null float64 35 2003 283 non-null float64 36 2004 283 non-null float64 37 2005 283 non-null float64 38 2006 283 non-null float64 39 2007 283 non-null float64 40 2008 283 non-null float64 41 2009 283 non-null float64 42 2010 300 non-null float64 43 2011 300 non-null float64 44 2012 300 non-null float64 45 2013 300 non-null float64 46 2014 300 non-null float64 47 2015 300 non-null float64 48 2016 300 non-null float64 49 2017 300 non-null float64 50 2018 300 non-null float64 51 2019 300 non-null float64 52 2020 300 non-null float64 dtypes: float64(51), object(2) memory usage: 126.0+ KB
df.describe()
| 1970 | 1971 | 1972 | 1973 | 1974 | 1975 | 1976 | 1977 | 1978 | 1979 | ... | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2.840000e+02 | 2.840000e+02 | 2.840000e+02 | 2.840000e+02 | 2.840000e+02 | 2.840000e+02 | 2.840000e+02 | 2.840000e+02 | 2.840000e+02 | 2.840000e+02 | ... | 3.000000e+02 | 3.000000e+02 | 3.000000e+02 | 3.000000e+02 | 3.000000e+02 | 3.000000e+02 | 3.000000e+02 | 3.000000e+02 | 3.000000e+02 | 3.000000e+02 |
| mean | 9.011960e+04 | 9.429002e+04 | 9.581337e+04 | 9.975289e+04 | 9.443671e+04 | 9.668779e+04 | 1.000583e+05 | 1.058091e+05 | 1.052697e+05 | 1.111674e+05 | ... | 1.269225e+05 | 1.261850e+05 | 1.279828e+05 | 1.270545e+05 | 1.289333e+05 | 1.301465e+05 | 1.326062e+05 | 1.345600e+05 | 1.352105e+05 | 1.195288e+05 |
| std | 4.455932e+05 | 4.663730e+05 | 4.742706e+05 | 4.898451e+05 | 4.605783e+05 | 4.732360e+05 | 4.900167e+05 | 5.219472e+05 | 5.213416e+05 | 5.446264e+05 | ... | 5.928098e+05 | 5.891410e+05 | 5.942789e+05 | 5.898692e+05 | 6.039160e+05 | 6.113764e+05 | 6.211631e+05 | 6.268406e+05 | 6.290843e+05 | 5.471620e+05 |
| min | 0.000000e+00 | 0.000000e+00 | -1.000000e-01 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | ... | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 |
| 25% | 7.300000e-01 | 7.925000e-01 | 8.075000e-01 | 1.000000e+00 | 1.725000e+00 | 2.110000e+00 | 2.320000e+00 | 2.365000e+00 | 2.642500e+00 | 3.550000e+00 | ... | 2.162500e+01 | 1.679000e+01 | 1.609250e+01 | 1.597000e+01 | 1.163000e+01 | 1.034000e+01 | 1.194000e+01 | 1.481000e+01 | 1.342000e+01 | 1.025000e+01 |
| 50% | 4.890000e+00 | 5.270000e+00 | 5.580000e+00 | 5.330000e+00 | 7.940000e+00 | 8.720000e+00 | 1.064500e+01 | 1.485000e+01 | 1.840000e+01 | 2.970000e+01 | ... | 4.640000e+01 | 4.377500e+01 | 5.430000e+01 | 5.805000e+01 | 5.195000e+01 | 5.004000e+01 | 4.950000e+01 | 5.775000e+01 | 6.017000e+01 | 4.744000e+01 |
| 75% | 4.302250e+02 | 4.692750e+02 | 5.253250e+02 | 6.664500e+02 | 8.611000e+02 | 1.258050e+03 | 1.463650e+03 | 1.897400e+03 | 1.787400e+03 | 2.088600e+03 | ... | 6.959725e+03 | 5.292775e+03 | 8.987525e+03 | 9.074500e+03 | 6.001700e+03 | 5.310900e+03 | 6.788900e+03 | 8.436250e+03 | 8.251150e+03 | 6.458750e+03 |
| max | 4.472838e+06 | 4.668262e+06 | 4.765624e+06 | 4.911626e+06 | 4.530411e+06 | 4.658922e+06 | 4.831220e+06 | 5.243449e+06 | 5.083948e+06 | 5.415031e+06 | ... | 5.685695e+06 | 5.661024e+06 | 5.670996e+06 | 5.582047e+06 | 5.694129e+06 | 5.663687e+06 | 5.664659e+06 | 5.668087e+06 | 5.641205e+06 | 4.869977e+06 |
8 rows × 51 columns
# transposed_df = df.set_index(['Data_Status', 'State', 'MSN']).T.reset_index()
# # Convert the Timestamp column to datetime
# transposed_df['Timestamp'] = pd.to_datetime(transposed_df['Timestamp'])
df# Assuming your DataFrame is called 'df'
df_trans = df.melt(id_vars=['State', 'MSN'], var_name='Year', value_name='Yearly Data')
# df['Year'] = pd.to_datetime(df['Year'], format='%Y')
# Set the 'State', 'MSN', and 'Year' columns as the index
df_trans.set_index(['State', 'MSN', 'Year'], inplace=True)
# Sort the index in ascending order
df_trans.sort_index(inplace=True)
# Print the resulting time series DataFrame
df_trans.reset_index(inplace=True)
df_trans['Year'] = pd.to_datetime(df_trans['Year'], format='%Y')
df_trans.head()
| State | MSN | Year | Yearly Data | |
|---|---|---|---|---|
| 0 | CA | ARICD | 1970-01-01 | 0.49 |
| 1 | CA | ARICD | 1971-01-01 | 0.64 |
| 2 | CA | ARICD | 1972-01-01 | 0.64 |
| 3 | CA | ARICD | 1973-01-01 | 0.69 |
| 4 | CA | ARICD | 1974-01-01 | 1.47 |
os.makedirs('Plots/Arima_results_plots',exist_ok=True)
for State in df_trans['State'].unique():
for msn in df_trans['MSN'].unique():
try:
fig = go.Figure()
print('State : {} and MSN : {}'.format(State,msn))
# Get the energy consumption data for the current country and sector
df_filter = df_trans[(df_trans['State'] == State) & (
df_trans['MSN'] == msn)][['Year', 'Yearly Data']]
df_filter_index = df_filter.set_index('Year')
train_data = df_filter[:-5]
test_data = df_filter[-5:]
# Prepare the data for modeling
years = df_filter_index.index
energy_consumption = df_filter_index.values.flatten()
# Split the data into training and testing
# Use all data except the last 5 years for training
Horizan = -5
train_data = energy_consumption[:Horizan]
test_data = energy_consumption[Horizan:] # Use the last 5 years for testing
# Fit the auto ARIMA model
model = auto_arima(train_data, seasonal=False)
model.fit(train_data)
# Generate predictions
predictions = model.predict(n_periods=len(test_data))
predictions_ahead_in_future = model.predict(n_periods=len(test_data)+15)
# Calculate evaluation metrics
mae = mean_absolute_error(test_data, predictions)
mse = mean_squared_error(test_data, predictions)
mape = np.mean(np.abs((test_data - predictions) / test_data)) * 100
print('Mean Absolute Error (MAE):', np.round(mae,2))
print('Mean Squared Error (MSE):', np.round(mse,2))
print('Mean Absolute Percentage Error (MAPE):', np.round(mape,2))
# Plot the training data
fig.add_trace(go.Scatter(
x=years[:Horizan], y=train_data, mode='lines+markers', name='Training Data'))
# Plot the predictions
fig.add_trace(go.Scatter(
x=years[Horizan:], y=test_data, mode='lines+markers', name='Actual'))
fig.add_trace(go.Scatter(
x=years[Horizan:], y=predictions, mode='lines+markers', name='Predicted'))
fig.add_trace(go.Scatter(
x=pd.date_range(start = years[Horizan],periods=15,freq='Y'), y=predictions_ahead_in_future, mode='lines+markers', name='Prediction till 2030'))
# Update the layout
fig.update_layout(title=f'Energy Consumption Forecast State using ARIMA : {State} : MSN : {msn} ',
xaxis_title='Year', yaxis_title='Energy Consumption')
# Show the plot
fig.show()
print(State,msn)
fig.write_image(f'Plots/Arima_results_plots/{State}_{msn}.png')
# break
except:
print('Error occoured in Combination State : {} and MSN : {} Due NaN Value'.format(State,mse))
State : CA and MSN : ARICD Mean Absolute Error (MAE): 1.7 Mean Squared Error (MSE): 3.99 Mean Absolute Percentage Error (MAPE): 15.68
CA ARICD State : CA and MSN : ARICV Mean Absolute Error (MAE): 254.19 Mean Squared Error (MSE): 68574.32 Mean Absolute Percentage Error (MAPE): 39.24
CA ARICV State : CA and MSN : ARTCD Mean Absolute Error (MAE): 1.7 Mean Squared Error (MSE): 3.99 Mean Absolute Percentage Error (MAPE): 15.68
CA ARTCD State : CA and MSN : ARTCV Mean Absolute Error (MAE): 254.19 Mean Squared Error (MSE): 68574.32 Mean Absolute Percentage Error (MAPE): 39.24
CA ARTCV State : CA and MSN : ARTXD Mean Absolute Error (MAE): 1.7 Mean Squared Error (MSE): 3.99 Mean Absolute Percentage Error (MAPE): 15.68
CA ARTXD State : CA and MSN : ARTXV Mean Absolute Error (MAE): 254.19 Mean Squared Error (MSE): 68574.32 Mean Absolute Percentage Error (MAPE): 39.24
CA ARTXV State : CA and MSN : AVACD Mean Absolute Error (MAE): 1.88 Mean Squared Error (MSE): 4.53 Mean Absolute Percentage Error (MAPE): 8.03
CA AVACD State : CA and MSN : AVACV Mean Absolute Error (MAE): 11.86 Mean Squared Error (MSE): 198.7 Mean Absolute Percentage Error (MAPE): 20.67
CA AVACV State : CA and MSN : AVTCD Mean Absolute Error (MAE): 1.88 Mean Squared Error (MSE): 4.53 Mean Absolute Percentage Error (MAPE): 8.03
CA AVTCD State : CA and MSN : AVTCV Mean Absolute Error (MAE): 11.86 Mean Squared Error (MSE): 198.7 Mean Absolute Percentage Error (MAPE): 20.67
CA AVTCV State : CA and MSN : AVTXD Mean Absolute Error (MAE): 1.88 Mean Squared Error (MSE): 4.53 Mean Absolute Percentage Error (MAPE): 8.03
CA AVTXD State : CA and MSN : AVTXV Mean Absolute Error (MAE): 11.86 Mean Squared Error (MSE): 198.7 Mean Absolute Percentage Error (MAPE): 20.67
CA AVTXV State : CA and MSN : CLACD Mean Absolute Error (MAE): 0.0 Mean Squared Error (MSE): 0.0 Mean Absolute Percentage Error (MAPE): nan
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: invalid value encountered in divide
CA CLACD State : CA and MSN : CLACV Mean Absolute Error (MAE): 0.0 Mean Squared Error (MSE): 0.0 Mean Absolute Percentage Error (MAPE): nan
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: invalid value encountered in divide
CA CLACV State : CA and MSN : CLCCD Mean Absolute Error (MAE): 0.46 Mean Squared Error (MSE): 0.24 Mean Absolute Percentage Error (MAPE): inf
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: divide by zero encountered in divide
CA CLCCD State : CA and MSN : CLCCV Mean Absolute Error (MAE): 0.0 Mean Squared Error (MSE): 0.0 Mean Absolute Percentage Error (MAPE): inf
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: divide by zero encountered in divide
CA CLCCV State : CA and MSN : CLEID Mean Absolute Error (MAE): 0.0 Mean Squared Error (MSE): 0.0 Mean Absolute Percentage Error (MAPE): nan
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: invalid value encountered in divide
CA CLEID State : CA and MSN : CLEIV Mean Absolute Error (MAE): 0.0 Mean Squared Error (MSE): 0.0 Mean Absolute Percentage Error (MAPE): nan
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: invalid value encountered in divide
CA CLEIV State : CA and MSN : CLICD Mean Absolute Error (MAE): 0.25 Mean Squared Error (MSE): 0.07 Mean Absolute Percentage Error (MAPE): 7.08
CA CLICD State : CA and MSN : CLICV Mean Absolute Error (MAE): 8.83 Mean Squared Error (MSE): 155.27 Mean Absolute Percentage Error (MAPE): 7.33
CA CLICV State : CA and MSN : CLISB Mean Absolute Error (MAE): 1821.8 Mean Squared Error (MSE): 4469817.4 Mean Absolute Percentage Error (MAPE): 5.82
CA CLISB State : CA and MSN : CLKCD Mean Absolute Error (MAE): 0.0 Mean Squared Error (MSE): 0.0 Mean Absolute Percentage Error (MAPE): nan
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: invalid value encountered in divide
CA CLKCD State : CA and MSN : CLKCV Mean Absolute Error (MAE): 0.0 Mean Squared Error (MSE): 0.0 Mean Absolute Percentage Error (MAPE): nan
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: invalid value encountered in divide
CA CLKCV State : CA and MSN : CLOCD Mean Absolute Error (MAE): 0.12 Mean Squared Error (MSE): 0.02 Mean Absolute Percentage Error (MAPE): 3.34
CA CLOCD State : CA and MSN : CLOCV Mean Absolute Error (MAE): 8.46 Mean Squared Error (MSE): 98.61 Mean Absolute Percentage Error (MAPE): 7.45
CA CLOCV State : CA and MSN : CLOSB Mean Absolute Error (MAE): 2803.27 Mean Squared Error (MSE): 9843655.25 Mean Absolute Percentage Error (MAPE): 8.65
CA CLOSB State : CA and MSN : CLRCD Mean Absolute Error (MAE): 1.13 Mean Squared Error (MSE): 1.43 Mean Absolute Percentage Error (MAPE): inf
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: divide by zero encountered in divide
CA CLRCD State : CA and MSN : CLRCV Mean Absolute Error (MAE): 0.27 Mean Squared Error (MSE): 0.08 Mean Absolute Percentage Error (MAPE): inf
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: divide by zero encountered in divide
CA CLRCV State : CA and MSN : CLRFB Mean Absolute Error (MAE): 0.0 Mean Squared Error (MSE): 0.0 Mean Absolute Percentage Error (MAPE): nan
/home/iffi/anaconda3/envs/sep_darts_2/lib/python3.11/site-packages/pmdarima/arima/auto.py:444: UserWarning: Input time-series is completely constant; returning a (0, 0, 0) ARMA. /tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: invalid value encountered in divide
CA CLRFB State : CA and MSN : CLSCB Mean Absolute Error (MAE): 4584.35 Mean Squared Error (MSE): 36777898.59 Mean Absolute Percentage Error (MAPE): 15.39
CA CLSCB State : CA and MSN : CLTCD Mean Absolute Error (MAE): 0.35 Mean Squared Error (MSE): 0.16 Mean Absolute Percentage Error (MAPE): 10.0
CA CLTCD State : CA and MSN : CLTCV Mean Absolute Error (MAE): 8.46 Mean Squared Error (MSE): 98.61 Mean Absolute Percentage Error (MAPE): 7.45
CA CLTCV State : CA and MSN : CLTXD Mean Absolute Error (MAE): 0.25 Mean Squared Error (MSE): 0.07 Mean Absolute Percentage Error (MAPE): 7.03
CA CLTXD State : CA and MSN : CLTXV Mean Absolute Error (MAE): 8.66 Mean Squared Error (MSE): 152.71 Mean Absolute Percentage Error (MAPE): 7.18
CA CLTXV State : CA and MSN : DFACD Mean Absolute Error (MAE): 3.72 Mean Squared Error (MSE): 19.47 Mean Absolute Percentage Error (MAPE): 14.3
CA DFACD State : CA and MSN : DFACV Mean Absolute Error (MAE): 1919.54 Mean Squared Error (MSE): 5388374.74 Mean Absolute Percentage Error (MAPE): 15.5
CA DFACV State : CA and MSN : DFCCD Mean Absolute Error (MAE): 2.31 Mean Squared Error (MSE): 6.79 Mean Absolute Percentage Error (MAPE): 14.8
CA DFCCD State : CA and MSN : DFCCV Mean Absolute Error (MAE): 59.28 Mean Squared Error (MSE): 4821.16 Mean Absolute Percentage Error (MAPE): 22.45
CA DFCCV State : CA and MSN : DFEID Mean Absolute Error (MAE): 3.02 Mean Squared Error (MSE): 12.67 Mean Absolute Percentage Error (MAPE): 19.88
CA DFEID State : CA and MSN : DFEIV Mean Absolute Error (MAE): 4.09 Mean Squared Error (MSE): 18.7 Mean Absolute Percentage Error (MAPE): 76.06
CA DFEIV State : CA and MSN : DFICD Mean Absolute Error (MAE): 2.32 Mean Squared Error (MSE): 6.57 Mean Absolute Percentage Error (MAPE): 14.99
CA DFICD State : CA and MSN : DFICV Mean Absolute Error (MAE): 577.65 Mean Squared Error (MSE): 380916.81 Mean Absolute Percentage Error (MAPE): 58.96
CA DFICV State : CA and MSN : DFISB Mean Absolute Error (MAE): 10437.52 Mean Squared Error (MSE): 141936422.79 Mean Absolute Percentage Error (MAPE): 15.69
CA DFISB State : CA and MSN : DFRCD Mean Absolute Error (MAE): 2.09 Mean Squared Error (MSE): 5.05 Mean Absolute Percentage Error (MAPE): 11.07
CA DFRCD State : CA and MSN : DFRCV Mean Absolute Error (MAE): 4.48 Mean Squared Error (MSE): 21.45 Mean Absolute Percentage Error (MAPE): 54.12
CA DFRCV State : CA and MSN : DFRFB Mean Absolute Error (MAE): 198.32 Mean Squared Error (MSE): 47029.57 Mean Absolute Percentage Error (MAPE): 40.51
CA DFRFB State : CA and MSN : DFSCB Mean Absolute Error (MAE): 27305.47 Mean Squared Error (MSE): 1394074741.57 Mean Absolute Percentage Error (MAPE): 5.01
CA DFSCB State : CA and MSN : DFTCD Mean Absolute Error (MAE): 3.44 Mean Squared Error (MSE): 17.03 Mean Absolute Percentage Error (MAPE): 14.04
CA DFTCD State : CA and MSN : DFTCV Mean Absolute Error (MAE): 1900.58 Mean Squared Error (MSE): 5683211.77 Mean Absolute Percentage Error (MAPE): 13.67
CA DFTCV State : CA and MSN : DFTXD Mean Absolute Error (MAE): 3.44 Mean Squared Error (MSE): 17.0 Mean Absolute Percentage Error (MAPE): 14.02
CA DFTXD State : CA and MSN : DFTXV Mean Absolute Error (MAE): 1899.56 Mean Squared Error (MSE): 5677855.97 Mean Absolute Percentage Error (MAPE): 13.67
CA DFTXV State : CA and MSN : DKEID Mean Absolute Error (MAE): 3.02 Mean Squared Error (MSE): 12.67 Mean Absolute Percentage Error (MAPE): 19.88
CA DKEID State : CA and MSN : DKEIV Mean Absolute Error (MAE): 5.05 Mean Squared Error (MSE): 30.52 Mean Absolute Percentage Error (MAPE): 93.03
CA DKEIV State : CA and MSN : ELEXD Mean Absolute Error (MAE): 2.33 Mean Squared Error (MSE): 7.88 Mean Absolute Percentage Error (MAPE): 38.26
CA ELEXD State : CA and MSN : ELEXV Mean Absolute Error (MAE): 51.36 Mean Squared Error (MSE): 5867.1 Mean Absolute Percentage Error (MAPE): 65.77
CA ELEXV State : CA and MSN : ELIMD Mean Absolute Error (MAE): 1.37 Mean Squared Error (MSE): 2.32 Mean Absolute Percentage Error (MAPE): 15.52
CA ELIMD State : CA and MSN : ELIMV Mean Absolute Error (MAE): 205.61 Mean Squared Error (MSE): 64446.4 Mean Absolute Percentage Error (MAPE): 118.63
CA ELIMV State : CA and MSN : EMACV Error occoured in Combination State : CA and MSN : 64446.39978843976 Due NaN Value State : CA and MSN : EMCCV Error occoured in Combination State : CA and MSN : 64446.39978843976 Due NaN Value State : CA and MSN : EMICV Error occoured in Combination State : CA and MSN : 64446.39978843976 Due NaN Value State : CA and MSN : EMTCV Error occoured in Combination State : CA and MSN : 64446.39978843976 Due NaN Value State : CA and MSN : ESACD Mean Absolute Error (MAE): 1.85 Mean Squared Error (MSE): 3.94 Mean Absolute Percentage Error (MAPE): 7.02
CA ESACD State : CA and MSN : ESACV Mean Absolute Error (MAE): 11.68 Mean Squared Error (MSE): 196.99 Mean Absolute Percentage Error (MAPE): 18.06
CA ESACV State : CA and MSN : ESCCD Mean Absolute Error (MAE): 1.17 Mean Squared Error (MSE): 1.88 Mean Absolute Percentage Error (MAPE): 2.51
CA ESCCD State : CA and MSN : ESCCV Mean Absolute Error (MAE): 1244.64 Mean Squared Error (MSE): 1684633.44 Mean Absolute Percentage Error (MAPE): 6.72
CA ESCCV State : CA and MSN : ESICD Mean Absolute Error (MAE): 1.39 Mean Squared Error (MSE): 2.57 Mean Absolute Percentage Error (MAPE): 3.53
CA ESICD State : CA and MSN : ESICV Mean Absolute Error (MAE): 404.67 Mean Squared Error (MSE): 176126.33 Mean Absolute Percentage Error (MAPE): 6.74
CA ESICV State : CA and MSN : ESISB Mean Absolute Error (MAE): 10433.45 Mean Squared Error (MSE): 119021409.6 Mean Absolute Percentage Error (MAPE): 6.65
CA ESISB State : CA and MSN : ESRCD Mean Absolute Error (MAE): 2.5 Mean Squared Error (MSE): 8.93 Mean Absolute Percentage Error (MAPE): 4.38
CA ESRCD State : CA and MSN : ESRCV Mean Absolute Error (MAE): 875.95 Mean Squared Error (MSE): 1573149.88 Mean Absolute Percentage Error (MAPE): 4.83
CA ESRCV State : CA and MSN : ESRFB Mean Absolute Error (MAE): 1879.01 Mean Squared Error (MSE): 4576051.03 Mean Absolute Percentage Error (MAPE): 22.0
CA ESRFB State : CA and MSN : ESSCB Mean Absolute Error (MAE): 57265.0 Mean Squared Error (MSE): 3895964468.84 Mean Absolute Percentage Error (MAPE): 6.71
CA ESSCB State : CA and MSN : ESTCD Mean Absolute Error (MAE): 1.2 Mean Squared Error (MSE): 2.49 Mean Absolute Percentage Error (MAPE): 2.41
CA ESTCD State : CA and MSN : ESTCV Mean Absolute Error (MAE): 1032.26 Mean Squared Error (MSE): 1425054.18 Mean Absolute Percentage Error (MAPE): 2.53
CA ESTCV State : CA and MSN : ESTXD Mean Absolute Error (MAE): 1.2 Mean Squared Error (MSE): 2.49 Mean Absolute Percentage Error (MAPE): 2.41
CA ESTXD State : CA and MSN : ESTXV Mean Absolute Error (MAE): 1032.26 Mean Squared Error (MSE): 1425054.18 Mean Absolute Percentage Error (MAPE): 2.53
CA ESTXV State : CA and MSN : FNICD Mean Absolute Error (MAE): 0.0 Mean Squared Error (MSE): 0.0 Mean Absolute Percentage Error (MAPE): nan
/home/iffi/anaconda3/envs/sep_darts_2/lib/python3.11/site-packages/pmdarima/arima/auto.py:444: UserWarning: Input time-series is completely constant; returning a (0, 0, 0) ARMA. /tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: invalid value encountered in divide
CA FNICD State : CA and MSN : FNICV Mean Absolute Error (MAE): 0.0 Mean Squared Error (MSE): 0.0 Mean Absolute Percentage Error (MAPE): nan
/home/iffi/anaconda3/envs/sep_darts_2/lib/python3.11/site-packages/pmdarima/arima/auto.py:444: UserWarning: Input time-series is completely constant; returning a (0, 0, 0) ARMA. /tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: invalid value encountered in divide
CA FNICV State : CA and MSN : FOICD Mean Absolute Error (MAE): 0.0 Mean Squared Error (MSE): 0.0 Mean Absolute Percentage Error (MAPE): nan
/home/iffi/anaconda3/envs/sep_darts_2/lib/python3.11/site-packages/pmdarima/arima/auto.py:444: UserWarning: Input time-series is completely constant; returning a (0, 0, 0) ARMA. /tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: invalid value encountered in divide
/home/iffi/anaconda3/envs/sep_darts_2/lib/python3.11/site-packages/pmdarima/arima/auto.py:444: UserWarning: Input time-series is completely constant; returning a (0, 0, 0) ARMA.
CA FOICD State : CA and MSN : FOICV Mean Absolute Error (MAE): 0.0 Mean Squared Error (MSE): 0.0 Mean Absolute Percentage Error (MAPE): nan
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: invalid value encountered in divide
CA FOICV State : CA and MSN : FSICD Mean Absolute Error (MAE): 0.0 Mean Squared Error (MSE): 0.0 Mean Absolute Percentage Error (MAPE): nan
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: invalid value encountered in divide
CA FSICD State : CA and MSN : FSICV Mean Absolute Error (MAE): 0.0 Mean Squared Error (MSE): 0.0 Mean Absolute Percentage Error (MAPE): nan
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: invalid value encountered in divide
CA FSICV State : CA and MSN : GDPRV Error occoured in Combination State : CA and MSN : 0.0 Due NaN Value State : CA and MSN : GDPRX Error occoured in Combination State : CA and MSN : 0.0 Due NaN Value State : CA and MSN : HLACD Mean Absolute Error (MAE): 3.55 Mean Squared Error (MSE): 20.01 Mean Absolute Percentage Error (MAPE): 19.57
CA HLACD State : CA and MSN : HLACV Mean Absolute Error (MAE): 8.0 Mean Squared Error (MSE): 98.98 Mean Absolute Percentage Error (MAPE): 109.36
CA HLACV State : CA and MSN : HLCCD Mean Absolute Error (MAE): 1.77 Mean Squared Error (MSE): 7.99 Mean Absolute Percentage Error (MAPE): 10.72
CA HLCCD State : CA and MSN : HLCCV Mean Absolute Error (MAE): 32.99 Mean Squared Error (MSE): 2074.7 Mean Absolute Percentage Error (MAPE): 15.34
CA HLCCV State : CA and MSN : HLICD Mean Absolute Error (MAE): 4.0 Mean Squared Error (MSE): 31.81 Mean Absolute Percentage Error (MAPE): 20.47
CA HLICD State : CA and MSN : HLICV Mean Absolute Error (MAE): 46.56 Mean Squared Error (MSE): 3680.7 Mean Absolute Percentage Error (MAPE): 11.42
CA HLICV State : CA and MSN : HLISB Mean Absolute Error (MAE): 1137.0 Mean Squared Error (MSE): 1662693.8 Mean Absolute Percentage Error (MAPE): 5.76
CA HLISB State : CA and MSN : HLRCD Mean Absolute Error (MAE): 3.88 Mean Squared Error (MSE): 21.59 Mean Absolute Percentage Error (MAPE): 15.25
CA HLRCD State : CA and MSN : HLRCV Mean Absolute Error (MAE): 84.16 Mean Squared Error (MSE): 11312.86 Mean Absolute Percentage Error (MAPE): 12.11
CA HLRCV State : CA and MSN : HLRFB Mean Absolute Error (MAE): 818.0 Mean Squared Error (MSE): 724329.2 Mean Absolute Percentage Error (MAPE): 53.68
CA HLRFB State : CA and MSN : HLSCB Mean Absolute Error (MAE): 4849.11 Mean Squared Error (MSE): 26697958.3 Mean Absolute Percentage Error (MAPE): 8.46
CA HLSCB State : CA and MSN : HLTCD Mean Absolute Error (MAE): 3.13 Mean Squared Error (MSE): 18.11 Mean Absolute Percentage Error (MAPE): 13.82
CA HLTCD State : CA and MSN : HLTCV Mean Absolute Error (MAE): 181.37 Mean Squared Error (MSE): 65332.27 Mean Absolute Percentage Error (MAPE): 13.66
CA HLTCV State : CA and MSN : HLTXD Mean Absolute Error (MAE): 3.13 Mean Squared Error (MSE): 18.11 Mean Absolute Percentage Error (MAPE): 13.82
CA HLTXD State : CA and MSN : HLTXV Mean Absolute Error (MAE): 181.37 Mean Squared Error (MSE): 65332.27 Mean Absolute Percentage Error (MAPE): 13.66
CA HLTXV State : CA and MSN : JFACD Mean Absolute Error (MAE): 2.2 Mean Squared Error (MSE): 5.99 Mean Absolute Percentage Error (MAPE): 17.96
CA JFACD State : CA and MSN : JFACV Mean Absolute Error (MAE): 1988.42 Mean Squared Error (MSE): 4943337.72 Mean Absolute Percentage Error (MAPE): 34.39
CA JFACV State : CA and MSN : JFTCD Mean Absolute Error (MAE): 2.2 Mean Squared Error (MSE): 5.99 Mean Absolute Percentage Error (MAPE): 17.96
CA JFTCD State : CA and MSN : JFTCV Mean Absolute Error (MAE): 1988.42 Mean Squared Error (MSE): 4943337.72 Mean Absolute Percentage Error (MAPE): 34.39
CA JFTCV State : CA and MSN : JFTXD Mean Absolute Error (MAE): 2.2 Mean Squared Error (MSE): 5.99 Mean Absolute Percentage Error (MAPE): 17.96
CA JFTXD State : CA and MSN : JFTXV Mean Absolute Error (MAE): 1988.42 Mean Squared Error (MSE): 4943337.72 Mean Absolute Percentage Error (MAPE): 34.39
CA JFTXV State : CA and MSN : KSCCD Mean Absolute Error (MAE): 3.84 Mean Squared Error (MSE): 21.64 Mean Absolute Percentage Error (MAPE): 19.31
CA KSCCD State : CA and MSN : KSCCV Mean Absolute Error (MAE): 0.4 Mean Squared Error (MSE): 0.22 Mean Absolute Percentage Error (MAPE): 39.36
CA KSCCV State : CA and MSN : KSICD Mean Absolute Error (MAE): 2.84 Mean Squared Error (MSE): 10.21 Mean Absolute Percentage Error (MAPE): 18.38
CA KSICD State : CA and MSN : KSICV Mean Absolute Error (MAE): 0.44 Mean Squared Error (MSE): 0.46 Mean Absolute Percentage Error (MAPE): 288.2
CA KSICV State : CA and MSN : KSRCD Mean Absolute Error (MAE): 3.84 Mean Squared Error (MSE): 21.64 Mean Absolute Percentage Error (MAPE): 19.31
CA KSRCD State : CA and MSN : KSRCV Mean Absolute Error (MAE): 2.58 Mean Squared Error (MSE): 9.16 Mean Absolute Percentage Error (MAPE): 34.37
CA KSRCV State : CA and MSN : KSTCD Mean Absolute Error (MAE): 3.83 Mean Squared Error (MSE): 21.62 Mean Absolute Percentage Error (MAPE): 19.32
CA KSTCD State : CA and MSN : KSTCV Mean Absolute Error (MAE): 5.31 Mean Squared Error (MSE): 35.67 Mean Absolute Percentage Error (MAPE): 69.83
CA KSTCV State : CA and MSN : KSTXD Mean Absolute Error (MAE): 3.83 Mean Squared Error (MSE): 21.62 Mean Absolute Percentage Error (MAPE): 19.32
CA KSTXD State : CA and MSN : KSTXV Mean Absolute Error (MAE): 5.31 Mean Squared Error (MSE): 35.67 Mean Absolute Percentage Error (MAPE): 69.83
CA KSTXV State : CA and MSN : LUACD Mean Absolute Error (MAE): 2.39 Mean Squared Error (MSE): 7.0 Mean Absolute Percentage Error (MAPE): 3.29
CA LUACD State : CA and MSN : LUACV Mean Absolute Error (MAE): 192.71 Mean Squared Error (MSE): 45041.24 Mean Absolute Percentage Error (MAPE): 21.76
CA LUACV State : CA and MSN : LUICD Mean Absolute Error (MAE): 2.39 Mean Squared Error (MSE): 7.0 Mean Absolute Percentage Error (MAPE): 3.29
CA LUICD State : CA and MSN : LUICV Mean Absolute Error (MAE): 77.75 Mean Squared Error (MSE): 6905.58 Mean Absolute Percentage Error (MAPE): 10.11
CA LUICV State : CA and MSN : LUTCD Mean Absolute Error (MAE): 2.39 Mean Squared Error (MSE): 7.0 Mean Absolute Percentage Error (MAPE): 3.29
CA LUTCD State : CA and MSN : LUTCV Mean Absolute Error (MAE): 270.46 Mean Squared Error (MSE): 86707.62 Mean Absolute Percentage Error (MAPE): 16.29
CA LUTCV State : CA and MSN : LUTXD Mean Absolute Error (MAE): 2.39 Mean Squared Error (MSE): 7.0 Mean Absolute Percentage Error (MAPE): 3.29
CA LUTXD State : CA and MSN : LUTXV Mean Absolute Error (MAE): 270.46 Mean Squared Error (MSE): 86707.62 Mean Absolute Percentage Error (MAPE): 16.29
CA LUTXV State : CA and MSN : MGACD Mean Absolute Error (MAE): 2.4 Mean Squared Error (MSE): 6.84 Mean Absolute Percentage Error (MAPE): 9.81
CA MGACD State : CA and MSN : MGACV Mean Absolute Error (MAE): 5773.27 Mean Squared Error (MSE): 53630609.65 Mean Absolute Percentage Error (MAPE): 15.08
CA MGACV State : CA and MSN : MGCCD Mean Absolute Error (MAE): 2.4 Mean Squared Error (MSE): 6.84 Mean Absolute Percentage Error (MAPE): 9.81
CA MGCCD State : CA and MSN : MGCCV Mean Absolute Error (MAE): 1267.82 Mean Squared Error (MSE): 1630147.88 Mean Absolute Percentage Error (MAPE): 94.54
CA MGCCV State : CA and MSN : MGICD Mean Absolute Error (MAE): 2.4 Mean Squared Error (MSE): 6.84 Mean Absolute Percentage Error (MAPE): 9.81
CA MGICD State : CA and MSN : MGICV Mean Absolute Error (MAE): 72.9 Mean Squared Error (MSE): 7797.95 Mean Absolute Percentage Error (MAPE): 9.16
CA MGICV State : CA and MSN : MGTCD Mean Absolute Error (MAE): 2.4 Mean Squared Error (MSE): 6.84 Mean Absolute Percentage Error (MAPE): 9.81
CA MGTCD State : CA and MSN : MGTCV Mean Absolute Error (MAE): 5977.49 Mean Squared Error (MSE): 56171615.85 Mean Absolute Percentage Error (MAPE): 14.77
CA MGTCV State : CA and MSN : MGTPV Mean Absolute Error (MAE): 152.69 Mean Squared Error (MSE): 37880.91 Mean Absolute Percentage Error (MAPE): 14.97
CA MGTPV State : CA and MSN : MGTXD Mean Absolute Error (MAE): 2.4 Mean Squared Error (MSE): 6.84 Mean Absolute Percentage Error (MAPE): 9.81
CA MGTXD State : CA and MSN : MGTXV Mean Absolute Error (MAE): 5977.49 Mean Squared Error (MSE): 56171615.85 Mean Absolute Percentage Error (MAPE): 14.77
CA MGTXV State : CA and MSN : MSICD Mean Absolute Error (MAE): 2.93 Mean Squared Error (MSE): 10.61 Mean Absolute Percentage Error (MAPE): 17.7
CA MSICD State : CA and MSN : MSICV Mean Absolute Error (MAE): 22.74 Mean Squared Error (MSE): 736.96 Mean Absolute Percentage Error (MAPE): 33.82
CA MSICV State : CA and MSN : NGACD Mean Absolute Error (MAE): 0.48 Mean Squared Error (MSE): 0.31 Mean Absolute Percentage Error (MAPE): 5.7
CA NGACD State : CA and MSN : NGACV Mean Absolute Error (MAE): 42.49 Mean Squared Error (MSE): 2319.99 Mean Absolute Percentage Error (MAPE): 19.42
CA NGACV State : CA and MSN : NGASB Mean Absolute Error (MAE): 2609.13 Mean Squared Error (MSE): 10744583.23 Mean Absolute Percentage Error (MAPE): 9.97
CA NGASB State : CA and MSN : NGCCD Mean Absolute Error (MAE): 0.76 Mean Squared Error (MSE): 0.83 Mean Absolute Percentage Error (MAPE): 8.42
CA NGCCD State : CA and MSN : NGCCV Mean Absolute Error (MAE): 75.25 Mean Squared Error (MSE): 13981.47 Mean Absolute Percentage Error (MAPE): 3.24
CA NGCCV State : CA and MSN : NGEID Mean Absolute Error (MAE): 0.47 Mean Squared Error (MSE): 0.29 Mean Absolute Percentage Error (MAPE): 13.34
CA NGEID State : CA and MSN : NGEIV Mean Absolute Error (MAE): 1052.16 Mean Squared Error (MSE): 1188493.81 Mean Absolute Percentage Error (MAPE): 47.77
CA NGEIV State : CA and MSN : NGICD Mean Absolute Error (MAE): 0.82 Mean Squared Error (MSE): 0.78 Mean Absolute Percentage Error (MAPE): 11.53
CA NGICD State : CA and MSN : NGICV Mean Absolute Error (MAE): 307.06 Mean Squared Error (MSE): 141212.61 Mean Absolute Percentage Error (MAPE): 6.81
CA NGICV State : CA and MSN : NGISB Mean Absolute Error (MAE): 31877.8 Mean Squared Error (MSE): 2068623147.0 Mean Absolute Percentage Error (MAPE): 5.41
CA NGISB State : CA and MSN : NGLPB Mean Absolute Error (MAE): 12794.19 Mean Squared Error (MSE): 187676047.03 Mean Absolute Percentage Error (MAPE): 31.63
CA NGLPB State : CA and MSN : NGPZB Mean Absolute Error (MAE): 3041.4 Mean Squared Error (MSE): 11776586.2 Mean Absolute Percentage Error (MAPE): 14.02
CA NGPZB State : CA and MSN : NGRCD Mean Absolute Error (MAE): 0.67 Mean Squared Error (MSE): 0.69 Mean Absolute Percentage Error (MAPE): 5.26
CA NGRCD State : CA and MSN : NGRCV Mean Absolute Error (MAE): 666.0 Mean Squared Error (MSE): 653844.18 Mean Absolute Percentage Error (MAPE): 11.2
CA NGRCV State : CA and MSN : NGRFB Mean Absolute Error (MAE): 2075.46 Mean Squared Error (MSE): 7222604.39 Mean Absolute Percentage Error (MAPE): 1.36
CA NGRFB State : CA and MSN : NGSCB Mean Absolute Error (MAE): 182998.0 Mean Squared Error (MSE): 34469733075.6 Mean Absolute Percentage Error (MAPE): 9.24
CA NGSCB State : CA and MSN : NGTCD Mean Absolute Error (MAE): 0.91 Mean Squared Error (MSE): 1.03 Mean Absolute Percentage Error (MAPE): 12.02
CA NGTCD State : CA and MSN : NGTCV Mean Absolute Error (MAE): 781.39 Mean Squared Error (MSE): 801412.95 Mean Absolute Percentage Error (MAPE): 5.27
CA NGTCV State : CA and MSN : NGTXD Mean Absolute Error (MAE): 0.4 Mean Squared Error (MSE): 0.29 Mean Absolute Percentage Error (MAPE): 4.17
CA NGTXD State : CA and MSN : NGTXV Mean Absolute Error (MAE): 623.5 Mean Squared Error (MSE): 648774.15 Mean Absolute Percentage Error (MAPE): 4.82
CA NGTXV State : CA and MSN : NUEGD Mean Absolute Error (MAE): 0.11 Mean Squared Error (MSE): 0.01 Mean Absolute Percentage Error (MAPE): 17.08
CA NUEGD State : CA and MSN : NUEGV Mean Absolute Error (MAE): 6.94 Mean Squared Error (MSE): 88.9 Mean Absolute Percentage Error (MAPE): 6.09
CA NUEGV State : CA and MSN : NUETD Mean Absolute Error (MAE): 0.11 Mean Squared Error (MSE): 0.01 Mean Absolute Percentage Error (MAPE): 17.08
CA NUETD State : CA and MSN : NUETV Mean Absolute Error (MAE): 6.94 Mean Squared Error (MSE): 88.9 Mean Absolute Percentage Error (MAPE): 6.09
CA NUETV State : CA and MSN : OHICD Error occoured in Combination State : CA and MSN : 88.89800000000005 Due NaN Value State : CA and MSN : OHICV Error occoured in Combination State : CA and MSN : 88.89800000000005 Due NaN Value State : CA and MSN : OPICD Mean Absolute Error (MAE): 1.95 Mean Squared Error (MSE): 5.08 Mean Absolute Percentage Error (MAPE): 9.66
CA OPICD State : CA and MSN : OPICV Mean Absolute Error (MAE): 29.83 Mean Squared Error (MSE): 1083.98 Mean Absolute Percentage Error (MAPE): 13.72
CA OPICV State : CA and MSN : OPISB Mean Absolute Error (MAE): 507.0 Mean Squared Error (MSE): 319093.8 Mean Absolute Percentage Error (MAPE): 4.89
CA OPISB State : CA and MSN : OPSCB Mean Absolute Error (MAE): 507.0 Mean Squared Error (MSE): 319093.8 Mean Absolute Percentage Error (MAPE): 4.89
CA OPSCB State : CA and MSN : OPTCD Mean Absolute Error (MAE): 1.95 Mean Squared Error (MSE): 5.08 Mean Absolute Percentage Error (MAPE): 9.66
CA OPTCD State : CA and MSN : OPTCV Mean Absolute Error (MAE): 29.83 Mean Squared Error (MSE): 1083.98 Mean Absolute Percentage Error (MAPE): 13.72
CA OPTCV State : CA and MSN : OPTXD Mean Absolute Error (MAE): 1.95 Mean Squared Error (MSE): 5.08 Mean Absolute Percentage Error (MAPE): 9.66
CA OPTXD State : CA and MSN : OPTXV Mean Absolute Error (MAE): 29.83 Mean Squared Error (MSE): 1083.98 Mean Absolute Percentage Error (MAPE): 13.72
CA OPTXV State : CA and MSN : P1ICD Mean Absolute Error (MAE): 2.2 Mean Squared Error (MSE): 5.68 Mean Absolute Percentage Error (MAPE): 10.59
CA P1ICD State : CA and MSN : P1ICV Mean Absolute Error (MAE): 307.33 Mean Squared Error (MSE): 107497.02 Mean Absolute Percentage Error (MAPE): 18.97
CA P1ICV State : CA and MSN : P1ISB Mean Absolute Error (MAE): 3912.4 Mean Squared Error (MSE): 24062088.4 Mean Absolute Percentage Error (MAPE): 5.04
CA P1ISB State : CA and MSN : P1SCB Mean Absolute Error (MAE): 29898.27 Mean Squared Error (MSE): 1081859809.44 Mean Absolute Percentage Error (MAPE): 31.99
CA P1SCB State : CA and MSN : P1TCD Mean Absolute Error (MAE): 2.1 Mean Squared Error (MSE): 6.73 Mean Absolute Percentage Error (MAPE): 7.7
CA P1TCD State : CA and MSN : P1TCV Mean Absolute Error (MAE): 509.18 Mean Squared Error (MSE): 297006.22 Mean Absolute Percentage Error (MAPE): 19.75
CA P1TCV State : CA and MSN : P1TXD Mean Absolute Error (MAE): 2.1 Mean Squared Error (MSE): 6.27 Mean Absolute Percentage Error (MAPE): 7.77
CA P1TXD State : CA and MSN : P1TXV Mean Absolute Error (MAE): 509.18 Mean Squared Error (MSE): 297006.22 Mean Absolute Percentage Error (MAPE): 19.75
CA P1TXV State : CA and MSN : P5RFB Mean Absolute Error (MAE): 12060.8 Mean Squared Error (MSE): 159316599.6 Mean Absolute Percentage Error (MAPE): 7.2
CA P5RFB State : CA and MSN : PAACD Mean Absolute Error (MAE): 2.15 Mean Squared Error (MSE): 6.27 Mean Absolute Percentage Error (MAPE): 9.57
CA PAACD State : CA and MSN : PAACV Mean Absolute Error (MAE): 9359.64 Mean Squared Error (MSE): 116203233.36 Mean Absolute Percentage Error (MAPE): 15.69
CA PAACV State : CA and MSN : PACCD Mean Absolute Error (MAE): 2.22 Mean Squared Error (MSE): 7.25 Mean Absolute Percentage Error (MAPE): 10.94
CA PACCD State : CA and MSN : PACCV Mean Absolute Error (MAE): 205.52 Mean Squared Error (MSE): 68041.17 Mean Absolute Percentage Error (MAPE): 10.52
CA PACCV State : CA and MSN : PAEID Mean Absolute Error (MAE): 4.05 Mean Squared Error (MSE): 20.92 Mean Absolute Percentage Error (MAPE): 26.99
CA PAEID State : CA and MSN : PAEIV Mean Absolute Error (MAE): 17.98 Mean Squared Error (MSE): 323.76 Mean Absolute Percentage Error (MAPE): 339.24
CA PAEIV State : CA and MSN : PAICD Mean Absolute Error (MAE): 2.01 Mean Squared Error (MSE): 5.16 Mean Absolute Percentage Error (MAPE): 10.89
CA PAICD State : CA and MSN : PAICV Mean Absolute Error (MAE): 390.48 Mean Squared Error (MSE): 224686.96 Mean Absolute Percentage Error (MAPE): 10.68
CA PAICV State : CA and MSN : PAISB Mean Absolute Error (MAE): 13561.6 Mean Squared Error (MSE): 265144206.0 Mean Absolute Percentage Error (MAPE): 6.96
CA PAISB State : CA and MSN : PARCD Mean Absolute Error (MAE): 4.28 Mean Squared Error (MSE): 28.09 Mean Absolute Percentage Error (MAPE): 17.12
CA PARCD State : CA and MSN : PARCV Mean Absolute Error (MAE): 86.36 Mean Squared Error (MSE): 11995.48 Mean Absolute Percentage Error (MAPE): 12.12
CA PARCV State : CA and MSN : PASCB Mean Absolute Error (MAE): 234462.8 Mean Squared Error (MSE): 78729393216.4 Mean Absolute Percentage Error (MAPE): 7.98
CA PASCB State : CA and MSN : PATCD Mean Absolute Error (MAE): 2.23 Mean Squared Error (MSE): 5.67 Mean Absolute Percentage Error (MAPE): 10.46
CA PATCD State : CA and MSN : PATCV Mean Absolute Error (MAE): 10001.23 Mean Squared Error (MSE): 132830961.3 Mean Absolute Percentage Error (MAPE): 15.24
CA PATCV State : CA and MSN : PATXD Mean Absolute Error (MAE): 2.14 Mean Squared Error (MSE): 6.11 Mean Absolute Percentage Error (MAPE): 9.57
CA PATXD State : CA and MSN : PATXV Mean Absolute Error (MAE): 10001.04 Mean Squared Error (MSE): 132845671.5 Mean Absolute Percentage Error (MAPE): 15.24
CA PATXV State : CA and MSN : PCCCD Mean Absolute Error (MAE): 0.0 Mean Squared Error (MSE): 0.0 Mean Absolute Percentage Error (MAPE): nan
/home/iffi/anaconda3/envs/sep_darts_2/lib/python3.11/site-packages/pmdarima/arima/auto.py:444: UserWarning: Input time-series is completely constant; returning a (0, 0, 0) ARMA. /tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: invalid value encountered in divide
CA PCCCD State : CA and MSN : PCCCV Mean Absolute Error (MAE): 0.0 Mean Squared Error (MSE): 0.0 Mean Absolute Percentage Error (MAPE): nan
/home/iffi/anaconda3/envs/sep_darts_2/lib/python3.11/site-packages/pmdarima/arima/auto.py:444: UserWarning: Input time-series is completely constant; returning a (0, 0, 0) ARMA. /tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: invalid value encountered in divide
CA PCCCV State : CA and MSN : PCEID Mean Absolute Error (MAE): 0.0 Mean Squared Error (MSE): 0.0 Mean Absolute Percentage Error (MAPE): nan
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: invalid value encountered in divide
CA PCEID State : CA and MSN : PCEIV Mean Absolute Error (MAE): 0.0 Mean Squared Error (MSE): 0.0 Mean Absolute Percentage Error (MAPE): nan
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: invalid value encountered in divide
CA PCEIV State : CA and MSN : PCICD Mean Absolute Error (MAE): 0.44 Mean Squared Error (MSE): 0.48 Mean Absolute Percentage Error (MAPE): inf
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: divide by zero encountered in divide
CA PCICD State : CA and MSN : PCICV Mean Absolute Error (MAE): 0.97 Mean Squared Error (MSE): 3.35 Mean Absolute Percentage Error (MAPE): inf
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: divide by zero encountered in divide
CA PCICV State : CA and MSN : PCISB Mean Absolute Error (MAE): 484.27 Mean Squared Error (MSE): 1170721.59 Mean Absolute Percentage Error (MAPE): inf
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: divide by zero encountered in divide
CA PCISB State : CA and MSN : PCRFB Mean Absolute Error (MAE): 9083.4 Mean Squared Error (MSE): 109537385.0 Mean Absolute Percentage Error (MAPE): 14.2
CA PCRFB State : CA and MSN : PCSCB Mean Absolute Error (MAE): 484.0 Mean Squared Error (MSE): 1171280.0 Mean Absolute Percentage Error (MAPE): nan
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: invalid value encountered in divide
CA PCSCB State : CA and MSN : PCTCD Mean Absolute Error (MAE): 0.32 Mean Squared Error (MSE): 0.5 Mean Absolute Percentage Error (MAPE): nan
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: invalid value encountered in divide
CA PCTCD State : CA and MSN : PCTCV Mean Absolute Error (MAE): 0.76 Mean Squared Error (MSE): 2.89 Mean Absolute Percentage Error (MAPE): inf
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: divide by zero encountered in divide
CA PCTCV State : CA and MSN : PCTXD Mean Absolute Error (MAE): 0.44 Mean Squared Error (MSE): 0.48 Mean Absolute Percentage Error (MAPE): inf
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: divide by zero encountered in divide
CA PCTXD State : CA and MSN : PCTXV Mean Absolute Error (MAE): 0.97 Mean Squared Error (MSE): 3.35 Mean Absolute Percentage Error (MAPE): inf
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: divide by zero encountered in divide
CA PCTXV State : CA and MSN : PEACD Mean Absolute Error (MAE): 2.14 Mean Squared Error (MSE): 6.13 Mean Absolute Percentage Error (MAPE): 9.56
CA PEACD State : CA and MSN : PEACV Mean Absolute Error (MAE): 9362.87 Mean Squared Error (MSE): 116264624.49 Mean Absolute Percentage Error (MAPE): 15.63
CA PEACV State : CA and MSN : PEASB Mean Absolute Error (MAE): 212796.29 Mean Squared Error (MSE): 87854943137.53 Mean Absolute Percentage Error (MAPE): 8.28
CA PEASB State : CA and MSN : PECCD Mean Absolute Error (MAE): 0.63 Mean Squared Error (MSE): 0.5 Mean Absolute Percentage Error (MAPE): 5.25
CA PECCD State : CA and MSN : PECCV Mean Absolute Error (MAE): 404.97 Mean Squared Error (MSE): 246490.9 Mean Absolute Percentage Error (MAPE): 9.54
CA PECCV State : CA and MSN : PECSB Mean Absolute Error (MAE): 35670.53 Mean Squared Error (MSE): 1604596203.21 Mean Absolute Percentage Error (MAPE): 10.21
CA PECSB State : CA and MSN : PEEID Mean Absolute Error (MAE): 0.28 Mean Squared Error (MSE): 0.11 Mean Absolute Percentage Error (MAPE): 8.67
CA PEEID State : CA and MSN : PEEIV Mean Absolute Error (MAE): 1237.59 Mean Squared Error (MSE): 1650669.04 Mean Absolute Percentage Error (MAPE): 44.83
CA PEEIV State : CA and MSN : PEICD Mean Absolute Error (MAE): 0.86 Mean Squared Error (MSE): 0.97 Mean Absolute Percentage Error (MAPE): 9.29
CA PEICD State : CA and MSN : PEICV Mean Absolute Error (MAE): 1282.37 Mean Squared Error (MSE): 2021647.25 Mean Absolute Percentage Error (MAPE): 15.68
CA PEICV State : CA and MSN : PEISB Mean Absolute Error (MAE): 29790.23 Mean Squared Error (MSE): 1849291764.27 Mean Absolute Percentage Error (MAPE): 3.52
CA PEISB State : CA and MSN : PERCD Mean Absolute Error (MAE): 0.4 Mean Squared Error (MSE): 0.26 Mean Absolute Percentage Error (MAPE): 3.0
CA PERCD State : CA and MSN : PERCV Mean Absolute Error (MAE): 570.4 Mean Squared Error (MSE): 499705.08 Mean Absolute Percentage Error (MAPE): 8.46
CA PERCV State : CA and MSN : PERSB Mean Absolute Error (MAE): 39980.4 Mean Squared Error (MSE): 2101231044.4 Mean Absolute Percentage Error (MAPE): 8.02
CA PERSB State : CA and MSN : PESCB Mean Absolute Error (MAE): 192606.0 Mean Squared Error (MSE): 136900170181.6 Mean Absolute Percentage Error (MAPE): 3.88
CA PESCB State : CA and MSN : PETCD Mean Absolute Error (MAE): 1.39 Mean Squared Error (MSE): 2.28 Mean Absolute Percentage Error (MAPE): 8.87
CA PETCD State : CA and MSN : PETCV Mean Absolute Error (MAE): 10529.52 Mean Squared Error (MSE): 138679293.62 Mean Absolute Percentage Error (MAPE): 12.05
CA PETCV State : CA and MSN : PETXD Mean Absolute Error (MAE): 1.58 Mean Squared Error (MSE): 2.79 Mean Absolute Percentage Error (MAPE): 9.02
CA PETXD State : CA and MSN : PETXV Mean Absolute Error (MAE): 9990.82 Mean Squared Error (MSE): 130510776.64 Mean Absolute Percentage Error (MAPE): 12.53
CA PETXV State : CA and MSN : PQACD Error occoured in Combination State : CA and MSN : 130510776.64079988 Due NaN Value State : CA and MSN : PQACV Error occoured in Combination State : CA and MSN : 130510776.64079988 Due NaN Value State : CA and MSN : PQCCD Error occoured in Combination State : CA and MSN : 130510776.64079988 Due NaN Value State : CA and MSN : PQCCV Error occoured in Combination State : CA and MSN : 130510776.64079988 Due NaN Value State : CA and MSN : PQICD Error occoured in Combination State : CA and MSN : 130510776.64079988 Due NaN Value State : CA and MSN : PQICV Error occoured in Combination State : CA and MSN : 130510776.64079988 Due NaN Value State : CA and MSN : PQISB Error occoured in Combination State : CA and MSN : 130510776.64079988 Due NaN Value State : CA and MSN : PQRCD Error occoured in Combination State : CA and MSN : 130510776.64079988 Due NaN Value State : CA and MSN : PQRCV Error occoured in Combination State : CA and MSN : 130510776.64079988 Due NaN Value State : CA and MSN : PQRFB Error occoured in Combination State : CA and MSN : 130510776.64079988 Due NaN Value State : CA and MSN : PQSCB Error occoured in Combination State : CA and MSN : 130510776.64079988 Due NaN Value State : CA and MSN : PQTCD Error occoured in Combination State : CA and MSN : 130510776.64079988 Due NaN Value State : CA and MSN : PQTCV Error occoured in Combination State : CA and MSN : 130510776.64079988 Due NaN Value State : CA and MSN : PQTXD Error occoured in Combination State : CA and MSN : 130510776.64079988 Due NaN Value State : CA and MSN : PQTXV Error occoured in Combination State : CA and MSN : 130510776.64079988 Due NaN Value State : CA and MSN : RFACD Mean Absolute Error (MAE): 2.12 Mean Squared Error (MSE): 5.78 Mean Absolute Percentage Error (MAPE): 18.62
CA RFACD State : CA and MSN : RFACV Mean Absolute Error (MAE): 641.26 Mean Squared Error (MSE): 658262.62 Mean Absolute Percentage Error (MAPE): 28.05
CA RFACV State : CA and MSN : RFCCD Mean Absolute Error (MAE): 2.72 Mean Squared Error (MSE): 8.6 Mean Absolute Percentage Error (MAPE): inf
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: divide by zero encountered in divide
CA RFCCD State : CA and MSN : RFCCV Mean Absolute Error (MAE): 27.62 Mean Squared Error (MSE): 821.04 Mean Absolute Percentage Error (MAPE): inf
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: divide by zero encountered in divide
CA RFCCV State : CA and MSN : RFEID Mean Absolute Error (MAE): 8.56 Mean Squared Error (MSE): 73.84 Mean Absolute Percentage Error (MAPE): inf
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: divide by zero encountered in divide
CA RFEID State : CA and MSN : RFEIV Mean Absolute Error (MAE): 0.99 Mean Squared Error (MSE): 1.06 Mean Absolute Percentage Error (MAPE): inf
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: divide by zero encountered in divide
CA RFEIV State : CA and MSN : RFICD Mean Absolute Error (MAE): 4.02 Mean Squared Error (MSE): 35.54 Mean Absolute Percentage Error (MAPE): inf
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: divide by zero encountered in divide
CA RFICD State : CA and MSN : RFICV Mean Absolute Error (MAE): 0.26 Mean Squared Error (MSE): 0.18 Mean Absolute Percentage Error (MAPE): inf
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: divide by zero encountered in divide
CA RFICV State : CA and MSN : RFISB Mean Absolute Error (MAE): 26.4 Mean Squared Error (MSE): 1646.4 Mean Absolute Percentage Error (MAPE): inf
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: divide by zero encountered in divide
CA RFISB State : CA and MSN : RFRFB Mean Absolute Error (MAE): 153.08 Mean Squared Error (MSE): 27209.2 Mean Absolute Percentage Error (MAPE): inf
/tmp/ipykernel_14322/1611046908.py:39: RuntimeWarning: divide by zero encountered in divide
CA RFRFB State : CA and MSN : RFSCB Mean Absolute Error (MAE): 41554.0 Mean Squared Error (MSE): 2134018850.0 Mean Absolute Percentage Error (MAPE): 25.01
CA RFSCB State : CA and MSN : RFTCD Mean Absolute Error (MAE): 2.12 Mean Squared Error (MSE): 5.79 Mean Absolute Percentage Error (MAPE): 18.65
CA RFTCD State : CA and MSN : RFTCV Mean Absolute Error (MAE): 629.24 Mean Squared Error (MSE): 596966.56 Mean Absolute Percentage Error (MAPE): 28.26
CA RFTCV State : CA and MSN : RFTXD Mean Absolute Error (MAE): 2.12 Mean Squared Error (MSE): 5.79 Mean Absolute Percentage Error (MAPE): 18.65
CA RFTXD State : CA and MSN : RFTXV Mean Absolute Error (MAE): 658.04 Mean Squared Error (MSE): 723103.08 Mean Absolute Percentage Error (MAPE): 28.25
CA RFTXV State : CA and MSN : SNICD Mean Absolute Error (MAE): 1.73 Mean Squared Error (MSE): 3.82 Mean Absolute Percentage Error (MAPE): 8.23
CA SNICD State : CA and MSN : SNICV Mean Absolute Error (MAE): 12.67 Mean Squared Error (MSE): 196.15 Mean Absolute Percentage Error (MAPE): 12.21
CA SNICV State : CA and MSN : TEACD Mean Absolute Error (MAE): 2.14 Mean Squared Error (MSE): 6.11 Mean Absolute Percentage Error (MAPE): 9.56
CA TEACD State : CA and MSN : TEACV Mean Absolute Error (MAE): 9362.99 Mean Squared Error (MSE): 116336944.11 Mean Absolute Percentage Error (MAPE): 15.62
CA TEACV State : CA and MSN : TECCD Mean Absolute Error (MAE): 0.59 Mean Squared Error (MSE): 0.56 Mean Absolute Percentage Error (MAPE): 1.98
CA TECCD State : CA and MSN : TECCV Mean Absolute Error (MAE): 946.73 Mean Squared Error (MSE): 1207220.49 Mean Absolute Percentage Error (MAPE): 4.25
CA TECCV State : CA and MSN : TEGDS Error occoured in Combination State : CA and MSN : 1207220.4921927233 Due NaN Value State : CA and MSN : TEICD Mean Absolute Error (MAE): 0.64 Mean Squared Error (MSE): 0.61 Mean Absolute Percentage Error (MAPE): 4.78
CA TEICD State : CA and MSN : TEICV Mean Absolute Error (MAE): 1745.05 Mean Squared Error (MSE): 3385434.41 Mean Absolute Percentage Error (MAPE): 12.15
CA TEICV State : CA and MSN : TEPFB Mean Absolute Error (MAE): 59310.0 Mean Squared Error (MSE): 5829371086.0 Mean Absolute Percentage Error (MAPE): 2.7
CA TEPFB State : CA and MSN : TERCD Mean Absolute Error (MAE): 0.76 Mean Squared Error (MSE): 0.95 Mean Absolute Percentage Error (MAPE): 2.52
CA TERCD State : CA and MSN : TERCV Mean Absolute Error (MAE): 1614.19 Mean Squared Error (MSE): 4289289.73 Mean Absolute Percentage Error (MAPE): 6.55
CA TERCV State : CA and MSN : TERFB Mean Absolute Error (MAE): 47624.37 Mean Squared Error (MSE): 2375324819.51 Mean Absolute Percentage Error (MAPE): 12.1
CA TERFB State : CA and MSN : TETCD Mean Absolute Error (MAE): 1.19 Mean Squared Error (MSE): 1.81 Mean Absolute Percentage Error (MAPE): 5.39
CA TETCD State : CA and MSN : TETCV Mean Absolute Error (MAE): 10058.92 Mean Squared Error (MSE): 128352717.06 Mean Absolute Percentage Error (MAPE): 8.26
CA TETCV State : CA and MSN : TETPV Mean Absolute Error (MAE): 257.99 Mean Squared Error (MSE): 85871.79 Mean Absolute Percentage Error (MAPE): 8.41
CA TETPV State : CA and MSN : TETXD Mean Absolute Error (MAE): 1.19 Mean Squared Error (MSE): 1.81 Mean Absolute Percentage Error (MAPE): 5.39
CA TETXD State : CA and MSN : TETXV Mean Absolute Error (MAE): 10058.92 Mean Squared Error (MSE): 128352717.06 Mean Absolute Percentage Error (MAPE): 8.26
CA TETXV State : CA and MSN : TNASB Mean Absolute Error (MAE): 212749.91 Mean Squared Error (MSE): 88070372552.62 Mean Absolute Percentage Error (MAPE): 8.27
CA TNASB State : CA and MSN : TNCSB Mean Absolute Error (MAE): 13047.02 Mean Squared Error (MSE): 435707814.08 Mean Absolute Percentage Error (MAPE): 1.83
CA TNCSB State : CA and MSN : TNISB Mean Absolute Error (MAE): 40593.31 Mean Squared Error (MSE): 2735674924.74 Mean Absolute Percentage Error (MAPE): 4.02
CA TNISB State : CA and MSN : TNRSB Mean Absolute Error (MAE): 42069.6 Mean Squared Error (MSE): 2463471651.6 Mean Absolute Percentage Error (MAPE): 5.19
CA TNRSB State : CA and MSN : TNSCB Mean Absolute Error (MAE): 223630.62 Mean Squared Error (MSE): 119081125511.73 Mean Absolute Percentage Error (MAPE): 4.42
CA TNSCB State : CA and MSN : TPOPP Mean Absolute Error (MAE): 549.12 Mean Squared Error (MSE): 450279.25 Mean Absolute Percentage Error (MAPE): 1.39
CA TPOPP State : CA and MSN : WDRCD Mean Absolute Error (MAE): 0.95 Mean Squared Error (MSE): 1.32 Mean Absolute Percentage Error (MAPE): 9.54
CA WDRCD State : CA and MSN : WDRCV Mean Absolute Error (MAE): 17.62 Mean Squared Error (MSE): 355.47 Mean Absolute Percentage Error (MAPE): 18.12
CA WDRCV State : CA and MSN : WDRSB Mean Absolute Error (MAE): 1715.26 Mean Squared Error (MSE): 3899803.53 Mean Absolute Percentage Error (MAPE): 19.33
CA WDRSB State : CA and MSN : WDRXB Mean Absolute Error (MAE): 4011.31 Mean Squared Error (MSE): 18789003.91 Mean Absolute Percentage Error (MAPE): 32.07
CA WDRXB State : CA and MSN : WWCCD Mean Absolute Error (MAE): 0.39 Mean Squared Error (MSE): 0.16 Mean Absolute Percentage Error (MAPE): 18.67
CA WWCCD State : CA and MSN : WWCCV Mean Absolute Error (MAE): 7.86 Mean Squared Error (MSE): 68.35 Mean Absolute Percentage Error (MAPE): 34.02
CA WWCCV State : CA and MSN : WWCSB Mean Absolute Error (MAE): 2718.8 Mean Squared Error (MSE): 8255430.76 Mean Absolute Percentage Error (MAPE): 24.91
CA WWCSB State : CA and MSN : WWCXB Mean Absolute Error (MAE): 256.0 Mean Squared Error (MSE): 77899.2 Mean Absolute Percentage Error (MAPE): 11.81
CA WWCXB State : CA and MSN : WWEID Mean Absolute Error (MAE): 0.4 Mean Squared Error (MSE): 0.22 Mean Absolute Percentage Error (MAPE): 19.34
CA WWEID State : CA and MSN : WWEIV Mean Absolute Error (MAE): 55.1 Mean Squared Error (MSE): 3416.06 Mean Absolute Percentage Error (MAPE): 40.41
CA WWEIV State : CA and MSN : WWICD Mean Absolute Error (MAE): 0.35 Mean Squared Error (MSE): 0.18 Mean Absolute Percentage Error (MAPE): 14.44
CA WWICD State : CA and MSN : WWICV Mean Absolute Error (MAE): 14.37 Mean Squared Error (MSE): 422.92 Mean Absolute Percentage Error (MAPE): 19.56
CA WWICV State : CA and MSN : WWISB Mean Absolute Error (MAE): 5740.95 Mean Squared Error (MSE): 59514619.12 Mean Absolute Percentage Error (MAPE): 19.77
CA WWISB State : CA and MSN : WWIXB Mean Absolute Error (MAE): 1824.6 Mean Squared Error (MSE): 4190107.0 Mean Absolute Percentage Error (MAPE): 16.17
CA WWIXB State : CA and MSN : WWSCB Mean Absolute Error (MAE): 8306.4 Mean Squared Error (MSE): 72285704.4 Mean Absolute Percentage Error (MAPE): 7.6
CA WWSCB State : CA and MSN : WWTCD Mean Absolute Error (MAE): 0.32 Mean Squared Error (MSE): 0.18 Mean Absolute Percentage Error (MAPE): 11.44
CA WWTCD State : CA and MSN : WWTCV Mean Absolute Error (MAE): 194.9 Mean Squared Error (MSE): 38605.37 Mean Absolute Percentage Error (MAPE): 58.87
CA WWTCV State : CA and MSN : WWTXD Mean Absolute Error (MAE): 0.62 Mean Squared Error (MSE): 0.62 Mean Absolute Percentage Error (MAPE): 16.86
CA WWTXD State : CA and MSN : WWTXV Mean Absolute Error (MAE): 16.08 Mean Squared Error (MSE): 356.7 Mean Absolute Percentage Error (MAPE): 8.8
CA WWTXV State : CA and MSN : WXICD Mean Absolute Error (MAE): 4.37 Mean Squared Error (MSE): 29.15 Mean Absolute Percentage Error (MAPE): 15.02
CA WXICD State : CA and MSN : WXICV Mean Absolute Error (MAE): 12.38 Mean Squared Error (MSE): 224.26 Mean Absolute Percentage Error (MAPE): 31.97
CA WXICV State : CA and MSN : ZWCDP Mean Absolute Error (MAE): 114.15 Mean Squared Error (MSE): 16826.06 Mean Absolute Percentage Error (MAPE): 10.41
CA ZWCDP State : CA and MSN : ZWHDP Mean Absolute Error (MAE): 128.12 Mean Squared Error (MSE): 38280.58 Mean Absolute Percentage Error (MAPE): 4.65
CA ZWHDP
os.makedirs('Plots/Sarima_results_plots',exist_ok=True)
for State in df_trans['State'].unique():
for msn in df_trans['MSN'].unique():
try:
fig = go.Figure()
print('State : {} and MSN : {}'.format(State,msn))
# Get the energy consumption data for the current country and sector
df_filter = df_trans[(df_trans['State'] == State) & (
df_trans['MSN'] == msn)][['Year', 'Yearly Data']]
df_filter_index = df_filter.set_index('Year')
train_data = df_filter[:-5]
test_data = df_filter[-5:]
# Prepare the data for modeling
years = df_filter_index.index
energy_consumption = df_filter_index.values.flatten()
# Split the data into training and testing
# Use all data except the last 5 years for training
Horizan = -5
train_data = energy_consumption[:Horizan]
test_data = energy_consumption[Horizan:] # Use the last 5 years for testing
# Fit the auto ARIMA model
model = auto_arima(train_data, seasonal=True)
model.fit(train_data)
# Generate predictions
predictions = model.predict(n_periods=len(test_data))
predictions_ahead_in_future = model.predict(n_periods=len(test_data)+15)
# Calculate evaluation metrics
mae = mean_absolute_error(test_data, predictions)
mse = mean_squared_error(test_data, predictions)
mape = np.mean(np.abs((test_data - predictions) / test_data)) * 100
print('Mean Absolute Error (MAE):', np.round(mae,2))
print('Mean Squared Error (MSE):', np.round(mse,2))
print('Mean Absolute Percentage Error (MAPE):', np.round(mape,2))
# Plot the training data
fig.add_trace(go.Scatter(
x=years[:Horizan], y=train_data, mode='lines+markers', name='Training Data'))
# Plot the predictions
fig.add_trace(go.Scatter(
x=years[Horizan:], y=test_data, mode='lines+markers', name='Actual'))
fig.add_trace(go.Scatter(
x=years[Horizan:], y=predictions, mode='lines+markers', name='Predicted'))
fig.add_trace(go.Scatter(
x=pd.date_range(start = years[Horizan],periods=15,freq='Y'), y=predictions_ahead_in_future, mode='lines+markers', name='Prediction till 2030'))
# Update the layout
fig.update_layout(title=f'Energy Consumption Forecast State using SARIMA : {State} : MSN : {msn} ',
xaxis_title='Year', yaxis_title='Energy Consumption')
# Show the plot
fig.show()
print(State,msn)
fig.write_image(f'Plots/Sarima_results_plots/{State}_{msn}.png')
# break
except:
print('Error occoured in Combination State : {} and MSN : {} Due NaN Value'.format(State,mse))
break
State : CA and MSN : ARICD Mean Absolute Error (MAE): 1.7 Mean Squared Error (MSE): 3.99 Mean Absolute Percentage Error (MAPE): 15.68
CA ARICD